+
+ +
+

🖼️ Object Detection

+

Currently implemented conformal object detection methods are listed in this page.

+

Each of these wrappers conformalize object localization models that are passed +as argument in the object constructor.

+
+
+class deel.puncc.object_detection.SplitBoxWise(predictor, *, train=False, weight_func=None, method='additive', random_state=0)
+

Implementation of box-wise conformal object detection. For more info, +we refer the user to the theory overview page

+
+
Parameters:
+
    +

  • predictor (BasePredictor | Any) – a predictive model.

    • +

  • train (bool) – if False, prediction model(s) will not be (re)trained. +Defaults to False.

    • +

  • weight_func (callable) – function that takes as argument an array of +features X and returns associated “conformality” weights, defaults to +None.

    • +

  • method (str) – chose between “additive” or “multiplicative” box-wise +conformalization.

    • +

  • random_state (int) – random seed used when the user does not +provide a custom fit/calibration split in fit method.

    • +
+
+
Raises:
+

ValueError – if method is not ‘additive’ or ‘multiplicative’.

+
+
+

Example:

+
from deel.puncc.object_detection import SplitBoxWise
+import numpy as np
+
+from sklearn.datasets import make_regression
+from sklearn.model_selection import train_test_split
+from sklearn.ensemble import RandomForestRegressor
+
+from deel.puncc.metrics import object_detection_mean_coverage
+from deel.puncc.metrics import object_detection_mean_area
+
+# Generate a random regression problem
+X, y = make_regression(
+    n_samples=1000,
+    n_features=4,
+    n_informative=2,
+    n_targets=4,
+    random_state=0,
+    shuffle=False,
+)
+
+# Create dummy object localization data formated as (x1, y1, x2, y2)
+y = np.abs(y)
+x1 = np.min(y[:, :2], axis=1)
+y1 = np.min(y[:, 2:], axis=1)
+x2 = np.max(y[:, :2], axis=1)
+y2 = np.max(y[:, 2:], axis=1)
+y = np.array([x1, y1, x2, y2]).T
+
+
+# Split data into train and test
+X, X_test, y, y_test = train_test_split(X, y, test_size=0.2, random_state=0)
+
+# Split train data into fit and calibration
+X_fit, X_calib, y_fit, y_calib = train_test_split(
+    X, y, test_size=0.2, random_state=0
+)
+
+# Create a random forest model
+rf_model = RandomForestRegressor(n_estimators=100, random_state=0)
+
+# CP method initialization
+od_cp = SplitBoxWise(rf_model, method="multiplicative", train=True)
+
+# The call to `fit` trains the model and computes the nonconformity
+# scores on the calibration set
+od_cp.fit(X_fit=X_fit, y_fit=y_fit, X_calib=X_calib, y_calib=y_calib)
+
+# The predict method infers prediction intervals with respect to
+# the significance level alpha = 20%
+y_pred, box_inner, box_outer = od_cp.predict(X_test, alpha=0.2)
+
+# Compute marginal coverage and average width of the prediction intervals
+coverage = object_detection_mean_coverage(box_outer, y_test)
+average_area = object_detection_mean_area(box_outer)
+print(f"Marginal coverage: {np.round(coverage, 2)}")
+
+
+
+
+fit(*, X=None, y=None, fit_ratio=0.8, X_fit=None, y_fit=None, X_calib=None, y_calib=None, use_cached=False, **kwargs)
+

This method fits the models on the fit data +and computes nonconformity scores on calibration data. +If (X, y) are provided, randomly split data into +fit and calib subsets w.r.t to the fit_ratio. +In case (X_fit, y_fit) and (X_calib, y_calib) are provided, +the conformalization is performed on the given user defined +fit and calibration sets.

+
+

Note

+

If X and y are provided, fit ignores +any user-defined fit/calib split.

+
+
+
Parameters:
+
    +

  • X (Iterable) – features from the training dataset.

    • +

  • y (Iterable) – labels from the training dataset.

    • +

  • fit_ratio (float) – the proportion of samples assigned to the +fit subset.

    • +

  • X_fit (Iterable) – features from the fit dataset.

    • +

  • y_fit (Iterable) – labels from the fit dataset.

    • +

  • X_calib (Iterable) – features from the calibration dataset.

    • +

  • y_calib (Iterable) – labels from the calibration dataset.

    • +

  • use_cached (bool) – if set, enables to add the previously computed +nonconformity scores (if any) to the pool estimated in the current +call to fit. The aggregation follows the CV+ +procedure.

    • +

  • kwargs (dict) – predict configuration to be passed to the model’s +fit method.

    • +
+
+
Raises:
+

RuntimeError – no dataset provided.

+
+
+
+ +
+
+get_nonconformity_scores()
+

Get computed nonconformity scores.

+
+
Returns:
+

computed nonconfomity scores.

+
+
Return type:
+

ndarray

+
+
+
+ +
+
+predict(X_test, alpha, correction_func=<function SplitBoxWise.<lambda>>)
+

Conformal object detection (w.r.t target miscoverage alpha) for +new samples.

+
+
Parameters:
+
    +

  • X_test (Iterable) – features of new samples.

    • +

  • alpha (float) – target maximum miscoverage.

    • +

  • correction_func (Callable) – correction for multiple hypothesis +testing in the case of multivariate regression. Defaults to +Bonferroni correction.

    • +
+
+
Returns:
+

y_pred, y_lower, y_higher

+
+
Return type:
+

Tuple[ndarray]

+
+
+
+ +
+ +
+ + +
+